[1]齐小刚,张旭,李家慧.机器学习桥接城市内涝与气候风险：研究进展与展望[J].智能系统学报,2026,21(5):1106-1129.[doi:10.11992/tis.202511003]
　QI Xiaogang,ZHANG Xu,LI Jiahui.Machine learning bridging urban waterlogging and climate risk: a review and outlook[J].CAAI transactions on intelligent systems,2026,21(5):1106-1129.[doi:10.11992/tis.202511003]
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机器学习桥接城市内涝与气候风险：研究进展与展望

《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷: 21 期数: 2026年第5期 页码: 1106-1129 栏目: 综述 出版日期: 2026-09-05
Title:
Machine learning bridging urban waterlogging and climate risk: a review and outlook
作者:
齐小刚1,2, 张旭1, 李家慧1,2
1. 西安电子科技大学 数学与统计学院, 陕西 西安 710071;
2. 西安市信息网络优化与数学方法重点实验室, 陕西 西安 710071
Author(s):
QI Xiaogang1,2, ZHANG Xu1, LI Jiahui1,2
1. School of Mathematics and Statistics, Xidian University, Xi’an 710071, China;
2. Xi’an Key Laboratory of Information Network Optimization and Mathematical Methods, Xi’an 710071, China
关键词:
机器学习; 城市内涝; 气候风险; 数据同化; 特征转换; 物理机理; 数据驱动; 不确定性量化; 风险传递
Keywords:
machine learning; urban waterlogging; climate risk; data assimilation; feature transformation; physical mechanism; data-driven; uncertainty quantification; risk transmission
分类号:
TP18；P4；TU998.4
DOI:
10.11992/tis.202511003
摘要:
气候变化和城市化进程共同导致了极端天气事件频发，城市内涝风险日益严峻，传统方法却难以应对气候科学与城市内涝之间的割裂。本文通过系统综述与批判性分析现有文献，首次总结提炼出机器学习作为连接两大领域的3种核心“桥梁”模式：多尺度数据同化与特征转换的桥梁、物理机理与数据驱动融合的建模桥梁、不确定性量化与风险传递的评估桥梁。首先剖析了两者的耦合机制及关键挑战；继而综述了支撑桥梁模式的关键技术；并通过具体案例阐述了每种模式如何实质性解决相关核心问题。本文所归纳的研究框架为理解并应对气候变化下的城市内涝风险提供了新视角。
Abstract:
Climate change and urbanization have led to frequent extreme weather events and severe urban waterlogging risks, while conventional methods fail to bridge the gap between climate science and urban waterlogging management. Through systematic review and critical analysis, this paper for the first time summarizes three core “bridging” modes of machine learning connecting the two domains: multi-scale data assimilation and feature transformation bridge, modeling bridge integrating physical mechanisms and data-driven approaches, and assessment bridge for uncertainty quantification and risk transmission. This paper first analyzes the coupling mechanism between climate science and urban waterlogging research and key challenges, then reviews key technologies supporting these bridging modes, and elaborates how each mode addresses core issues through case studies. This summarized framework provides a novel perspective for understanding and mitigating urban waterlogging risks under climate change.
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备注/Memo

收稿日期:2025-11-6。
基金项目:国家自然科学基金项目(62372354，62373291).
作者简介:齐小刚，教授，博士生导师，博士，主要研究方向为复杂系统建模与仿真、网络算法设计与应用。主持国家自然科学基金项目等国家和省部级项目20余项。发表学术论文100余篇。E-mail：xgqi@xidian.edu.cn。;张旭，博士研究生，主要研究方向为数据挖掘、大数据与人工智能、城市内涝自然灾害预防与治理。E-mail：zhangxu_0984@163.com。;李家慧，博士后，助理研究员，主要研究方向为数据处理、网络优化、故障诊断。E-mail：lijiahui@xidian.edu.cn。
通讯作者:齐小刚. E-mail：xgqi@xidian.edu.cn

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